一种基于层次聚类的空间非合作目标重构方法

由于激光传感器内在的缺陷，获得的非合作目标原始点云数据往往处于一个分布不均匀的状态，这就对后续的高质量非合作目标表面重构带来了很大的挑战.提出了一种基于全局约束的局部层次聚类方法来提升非合作目标点云分布的连续性.该方法主要可分为两步：1.基于全局约束的自适应八叉树三维空间分解，2.基于全局约束的层次聚类.第一步的主要目的是为了降低算法的复杂度，第二步则将分布不均匀的点集转化为均匀分布的状态.本文在三个非合作目标模型上进行了实验.实验的可视化结果与定量计算结果均验证了该方法的有效性.

A Spatial Non cooperative Target Reconstruction TechnologyBased on Hierarchical Clustering

Due to the inherent defects of laser sensors, the original point cloud of non cooperate targets is usually irregularly distributed, which brings great challenges to high quality 3D surface reconstruction of non cooperate targets. In this paper, we propose a local hierarchical clustering method based on global constraints to improve the consistency of non cooperative target point cloud distribution. Specifically, our method includes two main steps. The first one is the adaptive octree based 3D spatial decomposition with global constraints and the second one is hierarchical clustering based on global constraints. The main purpose of the former one is to reduce the complexity of the algorithm, and the later one aims to convert the non uniform point set to uniform one. Experiments are carried out on three non cooperative target models. The results of visualization and quantitative calculation verify the effectiveness of our method.